def testTrainBaselineModel(self):
csv_path = self.data_path + "annotations/manual/dev.corpus.csv"
default_types = [tf.string, tf.string, tf.string, tf.string]
if not os.path.isfile(csv_path):
self.skipTest(reason="Debug data not found.")
dataset = tf.data.experimental.CsvDataset(
filenames=csv_path,
record_defaults=default_types,
field_delim="|",
header=True,
)
dataset = dataset.map(
create_parse_fn(self.features_path, self.vocab_file))
def slice_fn(frames, label):
return frames[:32, :, :, :], label
dataset = dataset.map(slice_fn)
dataset = dataset.padded_batch(2,
padded_shapes=([None, 224, 224,
3], [None]))
model = BaselineModel(vocab_size=980)
model.compile(
optimizer="adam",
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=[WER(),
tf.keras.metrics.SparseCategoricalAccuracy()],
)
model.fit(dataset, validation_data=dataset, epochs=100)
def testParseExample(self):
if not os.path.isdir(self.features_path):
self.skipTest(reason="Debug data not found.")
parse_example = create_parse_fn(self.features_path, self.vocab_file)
frames, label = parse_example(self.id, self.folder, self.signer,
self.annotation)
self.assertEqual(frames.shape, (self.num_frames, 224, 224, 3))
self.assertAllEqual(label, self.tokenized_annotation)
def make_dataset() -> tf.data.Dataset:
dataset = tf.data.experimental.CsvDataset(
filenames=self.data_config["validation_csv"],
record_defaults=[tf.string, tf.string, tf.string, tf.string],
field_delim="|",
header=True,
)
dataset = dataset.map(
create_parse_fn(
self.data_config["features_path"] + "dev/",
self.data_config["vocab_file"],
))
return dataset
def testMakeDataset(self):
csv_path = self.data_path + "annotations/manual/dev.corpus.csv"
default_types = [tf.string, tf.string, tf.string, tf.string]
if not os.path.isfile(csv_path):
self.skipTest(reason="Debug data not found.")
dataset = tf.data.experimental.CsvDataset(
filenames=csv_path,
record_defaults=default_types,
field_delim="|",
header=True,
)
dataset = dataset.map(
create_parse_fn(self.features_path, self.vocab_file))